AdaGP-Rank: Applying boosting technique to genetic programming for learning to rank

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Wang:2010:YC-ICT,

• author = "Feng Wang2 and Xinshun Xu",
• title = "{AdaGP-Rank}: Applying boosting technique to genetic programming for learning to rank",
• booktitle = "IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT)",
• year = "2010",
• month = nov,
• pages = "259--262",
• abstract = "One crucial task of learning to rank in the field of information retrieval (IR) is to determine an ordering of documents according to their degree of relevance to the user given query. In this paper, a learning method is proposed named AdaGP-Rank by applying boosting techniques to genetic programming. This approach uses genetic programming to evolve ranking functions while a process inspired from AdaBoost technique helps the evolved ranking functions concentrate on the ranking of those documents associating those `hard' queries. Based on the confidence coefficients, the ranking functions obtained at each boosting round are then combined into a final strong ranker. Experiments conform that AdaGP-Rank has general better performance than several state-of-the-art ranking algorithms on the benchmark data sets.",
• keywords = "genetic algorithms, genetic programming, AdaBoost technique, AdaGP-Rank, boosting technique, confidence coefficients, document ordering, information retrieval, learning, user given query, document handling, learning (artificial intelligence), query processing",
• DOI = "doi:10.1109/YCICT.2010.5713094",
• notes = "Also known as \cite{5713094}",

}

Genetic Programming entries for Feng Wang2 Xinshun Xu

Citations